In order to evaluate supervised learning-based methods' performance on iMiGUE, 14 state-of-the-art algorithms are selected which can be simply categorized into four groups, namely, body key-points-based RNN (i.e., BLSTM, LSTM, and S-VAE [1]}), and GCN (i.e., ST-GCN [2]}, 2S-GCN [3]}, Shift-GCN [4]}, GCN-NAS [5]}, and MS-G3D [6]}), RGB-based 3DCNN (i.e., C3D [7]}, R3D-101 [8]}, and I3D [9]}), and 2DCNN with temporal reasoning (i.e., TSN [10]}, TRN [11]}, and TSM [12]}). We further evaluate the effectiveness of the proposed U-S-VAE by comparing it with existing unsupervised methods. In fact, only a few pose (skeleton)-based unsupervised models were proposed, e.g., LongT GAN [13]} and P&C [14]}. Here, we report the results of P&C since its implement code is publicly available. It is noted that all models follow the same evaluation protocol mentioned above for a fair comparison. In Table REF , we present the performances of these baseline networks.
